Modelling of hybrid deep learning framework with recursive feature elimination for distributed denial of service attack detection systems

Dealing with network security has always been a challenging task, particularly in the prevention and detection of distributed denial of service (DDoS) attacks. Attacks such as DDoS pose hazards to the system by compromising its accessibility to individuals who need to use a specific server. This type of cyberattack occurs when a system is overloaded with a massive amount of traffic, causing the network to become unavailable. This attack type focuses on engaging the service with correct operators without breaching safety parameters. Responsible artificial intelligence (AI) refers to the ethical development and deployment of AI systems that prioritise fairness, transparency, privacy, and accountability. Currently, the deep learning method is very effective in distinguishing DDoS traffic from harmless traffic by removing the representation of higher-level features from lower-level traffic. The study presented in this paper proposes a responsible artificial intelligence-based hybridisation framework for attack detection using recursive feature elimination (RAIHFAD-RFE) for cybersecurity systems. The study aimed to analyse and propose efficient cybersecurity tactics for preventing, mitigating and detecting DDoS attacks using advanced methods. As a primary step, the RAIHFAD-RFE technique utilises the Z-score standardisation method for the data pre-processing phase to clean, transform and organise raw data into a structured format. Furthermore, the recursive feature elimination (RFE) model is employed for feature selection (FS) to identify and retain the most essential features, thereby improving model performance and reducing model complexity. Moreover, the hybridisation of long short-term memory and bidirectional gated recurrent unit (LSTM-BiGRU) models was employed for classification. To optimise model performance, the improved orca predation algorithm (IOPA) is utilised for hyperparameter tuning to select the optimal parameters for enhanced accuracy. A comprehensive experimental analysis of the RAIHFAD-RFE approach was performed under the CIC-IDS-2017 and Edge-industrial internet of things (IIoT) datasets. A comparison study of the RAIHFAD-RFE approach provided superior accuracy values of 99.35% and 99.39%, respectively, compared to existing models on the dual dataset.